{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Выбор локации для скважины"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Описание проекта**\n",
    "\n",
    "Компании «ГлавРосГосНефть» нужно решить, где бурить новую скважину. Вам предоставлены пробы нефти в трёх регионах: в каждом 10 000 месторождений, где измерили качество нефти и объём её запасов. Постройте модель машинного обучения, которая поможет определить регион, где добыча принесёт наибольшую прибыль. Проанализируйте возможную прибыль и риски техникой *Bootstrap.*\n",
    "\n",
    "Шаги для выбора локации:\n",
    "\n",
    "- В избранном регионе ищут месторождения, для каждого определяют значения признаков;\n",
    "- Строят модель и оценивают объём запасов;\n",
    "- Выбирают месторождения с самым высокими оценками значений. Количество месторождений зависит от бюджета компании и стоимости разработки одной скважины;\n",
    "- Прибыль равна суммарной прибыли отобранных месторождений."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Инструкция по выполнению проекта**\n",
    "\n",
    "1. Загрузите и подготовьте данные. Поясните порядок действий.\n",
    "1. Обучите и проверьте модель для каждого региона:\n",
    "     1. Разбейте данные на обучающую и валидационную выборки в соотношении 75:25.\n",
    "     1. Обучите модель и сделайте предсказания на валидационной выборке.\n",
    "     1. Сохраните предсказания и правильные ответы на валидационной выборке.\n",
    "     1. Напечатайте на экране средний запас предсказанного сырья и RMSE модели.\n",
    "     1. Проанализируйте результаты.\n",
    "1. Подготовьтесь к расчёту прибыли:\n",
    "     1. Все ключевые значения для расчётов сохраните в отдельных переменных.\n",
    "     1. Рассчитайте достаточный объём сырья для безубыточной разработки новой скважины. Сравните полученный объём сырья со средним запасом в каждом регионе. \n",
    "     1. Напишите выводы по этапу подготовки расчёта прибыли.\n",
    "1. Напишите функцию для расчёта прибыли по выбранным скважинам и предсказаниям модели:\n",
    "     1. Выберите скважины с максимальными значениями предсказаний. \n",
    "     1. Просуммируйте целевое значение объёма сырья, соответствующее этим предсказаниям.\n",
    "     1. Рассчитайте прибыль для полученного объёма сырья.\n",
    "1. Посчитайте риски и прибыль для каждого региона:\n",
    "     1. Примените технику Bootstrap с 1000 выборок, чтобы найти распределение прибыли.\n",
    "     1. Найдите среднюю прибыль, 95%-й доверительный интервал и риск убытков. Убыток — это отрицательная прибыль.\n",
    "     1. Напишите выводы: предложите регион для разработки скважин и обоснуйте выбор."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Описание данных**\n",
    "\n",
    "Данные геологоразведки трёх регионов находятся в файлах: \n",
    "- `/datasets/geo_data_0.csv`.\n",
    "- `/datasets/geo_data_1.csv`.\n",
    "- `/datasets/geo_data_2.csv`.\n",
    "\n",
    "Атрибуты:\n",
    "- `id` — уникальный идентификатор скважины;\n",
    "- `f0`, `f1`, `f2` — три признака точек (неважно, что они означают, но сами признаки значимы);\n",
    "- `product` — объём запасов в скважине (тыс. баррелей)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Условия задачи**\n",
    "1. Для обучения модели подходит только линейная регрессия (остальные — недостаточно предсказуемые).\n",
    "1. При разведке региона исследуют 500 точек, из которых с помощью машинного обучения выбирают 200 лучших для разработки.\n",
    "1. Бюджет на разработку скважин в регионе — 10 млрд рублей.\n",
    "1. При нынешних ценах один баррель сырья приносит 450 рублей дохода. Доход с каждой единицы продукта составляет 450 тыс. рублей, поскольку объём указан в тысячах баррелей.\n",
    "1. После оценки рисков нужно оставить лишь те регионы, в которых вероятность убытков меньше 2.5%. Среди них выбирают регион с наибольшей средней прибылью.\n",
    "\n",
    "Данные синтетические: детали контрактов и характеристики месторождений не разглашаются."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Оглавление<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Загрузка-и-подготовка-данных\" data-toc-modified-id=\"Загрузка-и-подготовка-данных-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Загрузка и подготовка данных</a></span><ul class=\"toc-item\"><li><span><a href=\"#Подготовка-тетради-и-загрузка-данных\" data-toc-modified-id=\"Подготовка-тетради-и-загрузка-данных-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Подготовка тетради и загрузка данных</a></span></li><li><span><a href=\"#Анализ-данных-датафрейма-первого-региона\" data-toc-modified-id=\"Анализ-данных-датафрейма-первого-региона-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Анализ данных датафрейма первого региона</a></span></li><li><span><a href=\"#Анализ-данных-датафрейма-второго-региона\" data-toc-modified-id=\"Анализ-данных-датафрейма-второго-региона-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Анализ данных датафрейма второго региона</a></span></li><li><span><a href=\"#Анализ-данных-датафрейма-третьего-региона\" data-toc-modified-id=\"Анализ-данных-датафрейма-третьего-региона-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Анализ данных датафрейма третьего региона</a></span></li><li><span><a href=\"#Выводы-из-анализа-данных-датафреймов-всех-регионов\" data-toc-modified-id=\"Выводы-из-анализа-данных-датафреймов-всех-регионов-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Выводы из анализа данных датафреймов всех регионов</a></span></li></ul></li><li><span><a href=\"#Обучение-и-проверка-модели\" data-toc-modified-id=\"Обучение-и-проверка-модели-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Обучение и проверка модели</a></span><ul class=\"toc-item\"><li><span><a href=\"#Полезные-функции-подготовки-данных-и-подбора-моделей-и-их-параметров\" data-toc-modified-id=\"Полезные-функции-подготовки-данных-и-подбора-моделей-и-их-параметров-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Полезные функции подготовки данных и подбора моделей и их параметров</a></span></li><li><span><a href=\"#Модель-машинного-обучения-для-первого-региона\" data-toc-modified-id=\"Модель-машинного-обучения-для-первого-региона-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Модель машинного обучения для первого региона</a></span><ul class=\"toc-item\"><li><span><a href=\"#Подготовка-выборок-из-датафрейма-с-данными-первого-региона\" data-toc-modified-id=\"Подготовка-выборок-из-датафрейма-с-данными-первого-региона-2.2.1\"><span class=\"toc-item-num\">2.2.1&nbsp;&nbsp;</span>Подготовка выборок из датафрейма с данными первого региона</a></span></li><li><span><a href=\"#Модель-LinearRegression-с-использованием-технологий-Pipline-и-GridSearchSV-для-первого-региона\" data-toc-modified-id=\"Модель-LinearRegression-с-использованием-технологий-Pipline-и-GridSearchSV-для-первого-региона-2.2.2\"><span class=\"toc-item-num\">2.2.2&nbsp;&nbsp;</span>Модель LinearRegression с использованием технологий Pipline и GridSearchSV для первого региона</a></span></li><li><span><a href=\"#Предсказания-для-первого-региона\" data-toc-modified-id=\"Предсказания-для-первого-региона-2.2.3\"><span class=\"toc-item-num\">2.2.3&nbsp;&nbsp;</span>Предсказания для первого региона</a></span></li></ul></li><li><span><a href=\"#Модель-машинного-обучения-для-второго-региона\" data-toc-modified-id=\"Модель-машинного-обучения-для-второго-региона-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Модель машинного обучения для второго региона</a></span><ul class=\"toc-item\"><li><span><a href=\"#Подготовка-выборок-из-датафрейма-с-данными-второго-региона\" data-toc-modified-id=\"Подготовка-выборок-из-датафрейма-с-данными-второго-региона-2.3.1\"><span class=\"toc-item-num\">2.3.1&nbsp;&nbsp;</span>Подготовка выборок из датафрейма с данными второго региона</a></span></li><li><span><a href=\"#Модель-LinearRegression-с-использованием-технологий-Pipline-и-GridSearchSV-для-второго-региона-с-аттрибутом-f2,-сильно-коррелирующим-с-целевым-признаком\" data-toc-modified-id=\"Модель-LinearRegression-с-использованием-технологий-Pipline-и-GridSearchSV-для-второго-региона-с-аттрибутом-f2,-сильно-коррелирующим-с-целевым-признаком-2.3.2\"><span class=\"toc-item-num\">2.3.2&nbsp;&nbsp;</span>Модель LinearRegression с использованием технологий Pipline и GridSearchSV для второго региона с аттрибутом <code>f2</code>, сильно коррелирующим с целевым признаком</a></span></li><li><span><a href=\"#Предсказания-для-второго-региона\" data-toc-modified-id=\"Предсказания-для-второго-региона-2.3.3\"><span class=\"toc-item-num\">2.3.3&nbsp;&nbsp;</span>Предсказания для второго региона</a></span></li></ul></li><li><span><a href=\"#Модель-машинного-обучения-для-третьего-региона\" data-toc-modified-id=\"Модель-машинного-обучения-для-третьего-региона-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Модель машинного обучения для третьего региона</a></span><ul class=\"toc-item\"><li><span><a href=\"#Подготовка-выборок-из-датафрейма-с-данными-третьего-региона\" data-toc-modified-id=\"Подготовка-выборок-из-датафрейма-с-данными-третьего-региона-2.4.1\"><span class=\"toc-item-num\">2.4.1&nbsp;&nbsp;</span>Подготовка выборок из датафрейма с данными третьего региона</a></span></li><li><span><a href=\"#Модель-LinearRegression-с-использованием-технологий-Pipline-и-GridSearchSV-для-третьего-региона\" data-toc-modified-id=\"Модель-LinearRegression-с-использованием-технологий-Pipline-и-GridSearchSV-для-третьего-региона-2.4.2\"><span class=\"toc-item-num\">2.4.2&nbsp;&nbsp;</span>Модель LinearRegression с использованием технологий Pipline и GridSearchSV для третьего региона</a></span></li><li><span><a href=\"#Предсказания-для-третьего-региона\" data-toc-modified-id=\"Предсказания-для-третьего-региона-2.4.3\"><span class=\"toc-item-num\">2.4.3&nbsp;&nbsp;</span>Предсказания для третьего региона</a></span></li></ul></li><li><span><a href=\"#Анализ-результатов-обучения-и-проверки-моделей\" data-toc-modified-id=\"Анализ-результатов-обучения-и-проверки-моделей-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Анализ результатов обучения и проверки моделей</a></span></li></ul></li><li><span><a href=\"#Подготовка-к-расчёту-прибыли\" data-toc-modified-id=\"Подготовка-к-расчёту-прибыли-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Подготовка к расчёту прибыли</a></span><ul class=\"toc-item\"><li><span><a href=\"#Константы-проекта\" data-toc-modified-id=\"Константы-проекта-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Константы проекта</a></span></li><li><span><a href=\"#Точка-безубыточности\" data-toc-modified-id=\"Точка-безубыточности-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Точка безубыточности</a></span></li><li><span><a href=\"#Итоги-подготовки-к-расчету-прибыли\" data-toc-modified-id=\"Итоги-подготовки-к-расчету-прибыли-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Итоги подготовки к расчету прибыли</a></span></li><li><span><a href=\"#Функция-расчета-прибыли\" data-toc-modified-id=\"Функция-расчета-прибыли-3.4\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>Функция расчета прибыли</a></span></li></ul></li><li><span><a href=\"#Расчёт-прибыли-и-рисков\" data-toc-modified-id=\"Расчёт-прибыли-и-рисков-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Расчёт прибыли и рисков</a></span><ul class=\"toc-item\"><li><span><a href=\"#Функция-расчета-доходов-и-убытков\" data-toc-modified-id=\"Функция-расчета-доходов-и-убытков-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Функция расчета доходов и убытков</a></span></li><li><span><a href=\"#Расчет-ожидаемой-средней-прибыли-и-вероятности-убытков\" data-toc-modified-id=\"Расчет-ожидаемой-средней-прибыли-и-вероятности-убытков-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Расчет ожидаемой средней прибыли и вероятности убытков</a></span></li><li><span><a href=\"#Выводы-расчета-прибыли-и-рисков\" data-toc-modified-id=\"Выводы-расчета-прибыли-и-рисков-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>Выводы расчета прибыли и рисков</a></span></li></ul></li><li><span><a href=\"#Выводы-проекта\" data-toc-modified-id=\"Выводы-проекта-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Выводы проекта</a></span></li><li><span><a href=\"#Чек-лист-готовности-проекта\" data-toc-modified-id=\"Чек-лист-готовности-проекта-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Чек-лист готовности проекта</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Загрузка и подготовка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Подготовка тетради и загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Базовые библиотеки\n",
    "import pandas as pd # Датафреймы\n",
    "import numpy as np # Математика для массивов\n",
    "from math import factorial # Факториалы\n",
    "from scipy import stats as st # Статистика\n",
    "\n",
    "# Pipeline (пайплайн)\n",
    "from sklearn.pipeline import(\n",
    "    Pipeline, # Pipeline с ручным вводом названий шагов.\n",
    "    make_pipeline # Pipeline с автоматическим названием шагов.\n",
    ")\n",
    "\n",
    "# Функция для поддержки экспериментальной функции HavingGridSearchSV\n",
    "from sklearn.experimental import enable_halving_search_cv\n",
    "# Ускоренная автоматизация поиска лучших моделей и их параметров\n",
    "from sklearn.model_selection import HalvingGridSearchCV\n",
    "# Ускоренная автоматизация рандомного поиска лучших моделей и их параметров\n",
    "from sklearn.model_selection import HalvingRandomSearchCV\n",
    "\n",
    "# Автоматизация раздельного декодирования признаков\n",
    "from sklearn.compose import(\n",
    "    make_column_selector, \n",
    "    make_column_transformer, \n",
    "    ColumnTransformer\n",
    ")\n",
    "\n",
    "# Обработка данных для машинного обучения\n",
    "# Стандартизация данных\n",
    "import re\n",
    "from sklearn.preprocessing import(\n",
    "    OneHotEncoder, # Создание отдельных столбцов для каждого категориального значения, drop='first' (удаление первого столбца против dummy-ловушки), sparse=False (?)\n",
    "    OrdinalEncoder, # Кодирование порядковых категориальных признаков\n",
    "    LabelEncoder, \n",
    "    StandardScaler, \n",
    "    MinMaxScaler\n",
    ")\n",
    "from sklearn.utils import shuffle # Перемешивание данных для уравновешивания их в разных выборках\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor # Коэффициент инфляции дисперсии (5 и более - признак коррелирует со всеми остальными, его можно удалить и выразить через другие признаки)\n",
    "\n",
    "from sklearn.model_selection import(\n",
    "    GridSearchCV, # Поиск гиперпараметров по сетке (GridSearch)\n",
    "    train_test_split, # Разделение выборок с целевыми и нецелевыми признаками на обучающую и тестовую\n",
    "    validation_curve, \n",
    "    StratifiedKFold, # Кроссвалидация с указанием количества фолдов (частей, на которые будет разбита обучающая выборка, одна из которых будет участвовать в валидации)\n",
    "    KFold, # Кроссвалидация \n",
    "    cross_val_score # Оценка качества модели на кроссвалидации\n",
    ")\n",
    "\n",
    "# Различные модели машинного обучения (в данном проекте требуется регрессия)\n",
    "# (есть разбор на https://russianblogs.com/article/83691573909/)\n",
    "# Линейная модель\n",
    "from sklearn.linear_model import(\n",
    "    #LogisticRegression, # Линейная классификация\n",
    "    LinearRegression, # Линейная регрессия\n",
    "    Ridge , # Линейная регрессия. \"Хребтовая\" регрессия (метод наименьших квадратов)\n",
    "    BayesianRidge , # Линейная регрессия. Байесовская \"хребтовая\" регрессия (максимизации предельного логарифмического правдоподобия)\n",
    "    SGDRegressor # Линейная регрессия. SGD - Стохастический градиентный спуск (минимизирует регуляризованные эмпирические потери за счет стохастического градиентного спуска)\n",
    ")\n",
    "# Решающее дерево\n",
    "from sklearn.tree import(\n",
    "    #DecisionTreeClassifier, # Решающее дерево. Классификация\n",
    "    DecisionTreeRegressor # Решающее дерево. Регрессия\n",
    ")\n",
    "# Случайный лес\n",
    "from sklearn.ensemble import(\n",
    "    #RandomForestClassifier, # Случайный лес. Классификация\n",
    "    RandomForestRegressor # Случайный лес. Регрессия\n",
    ")\n",
    "# Машина опорных векторов\n",
    "from sklearn.svm import(\n",
    "    SVR # # Линейная модель. Регрессия с использованием опорных векторов\n",
    ")\n",
    "# Нейронная сеть\n",
    "from sklearn.neural_network import(\n",
    "    MLPRegressor # Нейронная сеть. Регрессия\n",
    ")\n",
    "\n",
    "# Метрики (Показатели качества моделей)\n",
    "from sklearn.metrics import(\n",
    "    # Метрики для моделей регрессии\n",
    "    mean_absolute_error, # MAE, Средняя абсолютная ошибка (не чувствительная к выбросам)\n",
    "    mean_absolute_percentage_error, # MAPE, Средняя абсолютная ошибка в % (универсальная в %)\n",
    "    mean_squared_error, # MSE, Средняя квадратичная ошибка (дисперсия, чувствительная к выбросам), RMSE (сигма) = mean_squared_error(test_y, preds, squared=False)\n",
    "    r2_score, # R^2, Коэффициент детерминации (универсальная в %, чувствительная к выбросам, может быть отрицательной и возвращать NaN)\n",
    "    \n",
    "    # Другое\n",
    "    ConfusionMatrixDisplay\n",
    ")\n",
    "\n",
    "# Визуализация графиков\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import rcParams, rcParamsDefault\n",
    "from pandas.plotting import scatter_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Отображение всех столбцов таблицы\n",
    "pd.set_option('display.max_columns', None)\n",
    "# Обязательно для нормального отображения графиков plt\n",
    "rcParams['figure.figsize'] = 10, 6\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "# Дополнительно и не обязательно для декорирования графиков\n",
    "factor = .8\n",
    "default_dpi = rcParamsDefault['figure.dpi']\n",
    "rcParams['figure.dpi'] = default_dpi * factor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Чтение файла с датафреймами\n",
    "try:\n",
    "    geo_data_0 = pd.read_csv('/datasets/geo_data_0.csv')\n",
    "    geo_data_1 = pd.read_csv('/datasets/geo_data_1.csv')\n",
    "    geo_data_2 = pd.read_csv('/datasets/geo_data_2.csv')\n",
    "except:\n",
    "    geo_data_0 = pd.read_csv('datasets/geo_data_0.csv')\n",
    "    geo_data_1 = pd.read_csv('datasets/geo_data_1.csv')\n",
    "    geo_data_2 = pd.read_csv('datasets/geo_data_2.csv')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализ данных датафрейма первого региона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 5 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   id       100000 non-null  object \n",
      " 1   f0       100000 non-null  float64\n",
      " 2   f1       100000 non-null  float64\n",
      " 3   f2       100000 non-null  float64\n",
      " 4   product  100000 non-null  float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 3.8+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>f0</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>txEyH</td>\n",
       "      <td>0.705745</td>\n",
       "      <td>-0.497823</td>\n",
       "      <td>1.221170</td>\n",
       "      <td>105.280062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2acmU</td>\n",
       "      <td>1.334711</td>\n",
       "      <td>-0.340164</td>\n",
       "      <td>4.365080</td>\n",
       "      <td>73.037750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>409Wp</td>\n",
       "      <td>1.022732</td>\n",
       "      <td>0.151990</td>\n",
       "      <td>1.419926</td>\n",
       "      <td>85.265647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iJLyR</td>\n",
       "      <td>-0.032172</td>\n",
       "      <td>0.139033</td>\n",
       "      <td>2.978566</td>\n",
       "      <td>168.620776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Xdl7t</td>\n",
       "      <td>1.988431</td>\n",
       "      <td>0.155413</td>\n",
       "      <td>4.751769</td>\n",
       "      <td>154.036647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id        f0        f1        f2     product\n",
       "0  txEyH  0.705745 -0.497823  1.221170  105.280062\n",
       "1  2acmU  1.334711 -0.340164  4.365080   73.037750\n",
       "2  409Wp  1.022732  0.151990  1.419926   85.265647\n",
       "3  iJLyR -0.032172  0.139033  2.978566  168.620776\n",
       "4  Xdl7t  1.988431  0.155413  4.751769  154.036647"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка датафрейма первого региона\n",
    "print(geo_data_0.info())\n",
    "geo_data_0.head() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f0</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.500419</td>\n",
       "      <td>0.250143</td>\n",
       "      <td>2.502647</td>\n",
       "      <td>92.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.871832</td>\n",
       "      <td>0.504433</td>\n",
       "      <td>3.248248</td>\n",
       "      <td>44.288691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.408605</td>\n",
       "      <td>-0.848218</td>\n",
       "      <td>-12.088328</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.072580</td>\n",
       "      <td>-0.200881</td>\n",
       "      <td>0.287748</td>\n",
       "      <td>56.497507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.502360</td>\n",
       "      <td>0.250252</td>\n",
       "      <td>2.515969</td>\n",
       "      <td>91.849972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.073581</td>\n",
       "      <td>0.700646</td>\n",
       "      <td>4.715088</td>\n",
       "      <td>128.564089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.362331</td>\n",
       "      <td>1.343769</td>\n",
       "      <td>16.003790</td>\n",
       "      <td>185.364347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  f0             f1             f2        product\n",
       "count  100000.000000  100000.000000  100000.000000  100000.000000\n",
       "mean        0.500419       0.250143       2.502647      92.500000\n",
       "std         0.871832       0.504433       3.248248      44.288691\n",
       "min        -1.408605      -0.848218     -12.088328       0.000000\n",
       "25%        -0.072580      -0.200881       0.287748      56.497507\n",
       "50%         0.502360       0.250252       2.515969      91.849972\n",
       "75%         1.073581       0.700646       4.715088     128.564089\n",
       "max         2.362331       1.343769      16.003790     185.364347"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Распределение значений датафрейма первого региона\n",
    "geo_data_0.describe() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество уникальных идентификаторов: 99990 < Количество объектов 100000\n"
     ]
    }
   ],
   "source": [
    "# Соответствие количества уникальных идентификаторов \n",
    "# и количества объектов в датафрейме первого региона\n",
    "# (сколько месторождений в регионе)\n",
    "geo_data_id = len(geo_data_0.id.unique())\n",
    "if geo_data_id == len(geo_data_0):\n",
    "    print(f'Количество уникальных идентификаторов: {geo_data_id} == Количество объектов {len(geo_data_0)}')\n",
    "else:\n",
    "    print(f'Количество уникальных идентификаторов: {geo_data_id} < Количество объектов {len(geo_data_0)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         id        f0        f1         f2     product\n",
      "931   HZww2  0.755284  0.368511   1.863211   30.681774\n",
      "7530  HZww2  1.061194 -0.373969  10.430210  158.828695\n",
      "\n",
      "          id        f0        f1        f2    product\n",
      "1364   bxg6G  0.411645  0.856830 -3.653440  73.604260\n",
      "41724  bxg6G -0.823752  0.546319  3.630479  93.007798\n",
      "\n",
      "          id        f0        f1        f2    product\n",
      "3389   A5aEY -0.039949  0.156872  0.209861  89.249364\n",
      "51970  A5aEY -0.180335  0.935548 -2.094773  33.020205\n",
      "\n",
      "          id        f0        f1        f2    product\n",
      "1949   QcMuo  0.506563 -0.323775 -2.215583  75.496502\n",
      "63593  QcMuo  0.635635 -0.473422  0.862670  64.578675\n",
      "\n",
      "          id        f0        f1        f2     product\n",
      "64022  74z30  0.741456  0.459229  5.153109  140.771492\n",
      "66136  74z30  1.084962 -0.312358  6.990771  127.643327\n",
      "\n",
      "          id        f0        f1        f2     product\n",
      "42529  AGS9W  1.454747 -0.479651  0.683380  126.370504\n",
      "69163  AGS9W -0.933795  0.116194 -3.655896   19.230453\n",
      "\n",
      "          id        f0        f1        f2    product\n",
      "21426  Tdehs  0.829407  0.298807 -0.049563  96.035308\n",
      "75715  Tdehs  0.112079  0.430296  3.218993  60.964018\n",
      "\n",
      "          id        f0        f1        f2     product\n",
      "16633  fiKDv  0.157341  1.028359  5.585586   95.817889\n",
      "90815  fiKDv  0.049883  0.841313  6.394613  137.346586\n",
      "\n",
      "          id        f0        f1        f2     product\n",
      "60140  TtcGQ  0.569276 -0.104876  6.440215   85.350186\n",
      "92341  TtcGQ  0.110711  1.022689  0.911381  101.318008\n",
      "\n",
      "          id        f0        f1         f2     product\n",
      "89582  bsk9y  0.398908 -0.400253  10.122376  163.433078\n",
      "97785  bsk9y  0.378429  0.005837   0.160827  160.637302\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Анализ объектов с дубликатами в аттрибуте \"id\"\n",
    "id = geo_data_0.loc[geo_data_0['id'].duplicated(), 'id'].values\n",
    "\n",
    "for i in id:\n",
    "    print(geo_data_0.loc[geo_data_0['id'] == i])\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Гистограммы числовых данных \n",
    "# первого региона\n",
    "geo_data_0.hist();\n"
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  },
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   "cell_type": "code",
   "execution_count": 9,
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    "# Анализ выбросов данных \n",
    "# первого региона\n",
    "for i in geo_data_0:\n",
    "    if i != 'id':\n",
    "        geo_data_0.boxplot(column=i);\n",
    "        plt.show() \n"
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   "execution_count": 10,
   "metadata": {},
   "outputs": [
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    "# Анализ корреляции аттрибутов\n",
    "# датафрейма первого региона\n",
    "geo_data_0.corr()\n"
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Коэффициент инфляции дисперсии аттрибутов третьего региона\n",
    "# (5 и более - признак коррелирует со всеми остальными признаками \n",
    "# и его можно удалить и выразить через другие признаки)\n",
    "geo_data = geo_data_0.select_dtypes('number')#.columns\n",
    "vif = [variance_inflation_factor(geo_data, i) for i in range(len(geo_data.columns))]\n",
    "_ = sns.barplot(x=geo_data.columns.tolist(), y=vif)\n",
    "\n",
    "vif\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализ данных датафрейма второго региона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 5 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   id       100000 non-null  object \n",
      " 1   f0       100000 non-null  float64\n",
      " 2   f1       100000 non-null  float64\n",
      " 3   f2       100000 non-null  float64\n",
      " 4   product  100000 non-null  float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 3.8+ MB\n",
      "None\n"
     ]
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       "      <th>product</th>\n",
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       "      <th>0</th>\n",
       "      <td>kBEdx</td>\n",
       "      <td>-15.001348</td>\n",
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       "      <td>62mP7</td>\n",
       "      <td>14.272088</td>\n",
       "      <td>-3.475083</td>\n",
       "      <td>0.999183</td>\n",
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       "      <th>4</th>\n",
       "      <td>AHL4O</td>\n",
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       "      id         f0         f1        f2     product\n",
       "0  kBEdx -15.001348  -8.276000 -0.005876    3.179103\n",
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     "execution_count": 12,
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   ],
   "source": [
    "# Проверка датафрейма второго региона\n",
    "print(geo_data_1.info())\n",
    "geo_data_1.head() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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       "      <th>count</th>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
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       "      <td>100000.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>1.141296</td>\n",
       "      <td>-4.796579</td>\n",
       "      <td>2.494541</td>\n",
       "      <td>68.825000</td>\n",
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       "      <th>std</th>\n",
       "      <td>8.965932</td>\n",
       "      <td>5.119872</td>\n",
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       "      <th>min</th>\n",
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       "      <th>25%</th>\n",
       "      <td>-6.298551</td>\n",
       "      <td>-8.267985</td>\n",
       "      <td>1.000021</td>\n",
       "      <td>26.953261</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.153055</td>\n",
       "      <td>-4.813172</td>\n",
       "      <td>2.011479</td>\n",
       "      <td>57.085625</td>\n",
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       "      <th>75%</th>\n",
       "      <td>8.621015</td>\n",
       "      <td>-1.332816</td>\n",
       "      <td>3.999904</td>\n",
       "      <td>107.813044</td>\n",
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       "      <th>max</th>\n",
       "      <td>29.421755</td>\n",
       "      <td>18.734063</td>\n",
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       "      <td>137.945408</td>\n",
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       "                  f0             f1             f2        product\n",
       "count  100000.000000  100000.000000  100000.000000  100000.000000\n",
       "mean        1.141296      -4.796579       2.494541      68.825000\n",
       "std         8.965932       5.119872       1.703572      45.944423\n",
       "min       -31.609576     -26.358598      -0.018144       0.000000\n",
       "25%        -6.298551      -8.267985       1.000021      26.953261\n",
       "50%         1.153055      -4.813172       2.011479      57.085625\n",
       "75%         8.621015      -1.332816       3.999904     107.813044\n",
       "max        29.421755      18.734063       5.019721     137.945408"
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     },
     "execution_count": 13,
     "metadata": {},
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    }
   ],
   "source": [
    "# Распределение значений датафрейма второго региона\n",
    "geo_data_1.describe() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество уникальных идентификаторов: 99996 < Количество объектов 100000\n"
     ]
    }
   ],
   "source": [
    "# Соответствие количества уникальных идентификаторов \n",
    "# и количества объектов в датафрейме второго региона\n",
    "# (сколько месторождений в регионе)\n",
    "geo_data_id = len(geo_data_1.id.unique())\n",
    "if geo_data_id == len(geo_data_1):\n",
    "    print(f'Количество уникальных идентификаторов: {geo_data_id} == Количество объектов {len(geo_data_1)}')\n",
    "else:\n",
    "    print(f'Количество уникальных идентификаторов: {geo_data_id} < Количество объектов {len(geo_data_1)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          id         f0        f1        f2    product\n",
      "1305   LHZR0  11.170835 -1.945066  3.002872  80.859783\n",
      "41906  LHZR0  -8.989672 -4.286607  2.009139  57.085625\n",
      "\n",
      "          id        f0        f1        f2     product\n",
      "2721   bfPNe -9.494442 -5.463692  4.006042  110.992147\n",
      "82178  bfPNe -6.202799 -4.820045  2.995107   84.038886\n",
      "\n",
      "          id         f0        f1        f2     product\n",
      "47591  wt4Uk  -9.091098 -8.109279 -0.002314    3.179103\n",
      "82873  wt4Uk  10.259972 -9.376355  4.994297  134.766305\n",
      "\n",
      "          id         f0         f1        f2     product\n",
      "5849   5ltQ6  -3.435401 -12.296043  1.999796   57.085625\n",
      "84461  5ltQ6  18.213839   2.191999  3.993869  107.813044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Анализ объектов с дубликатами в аттрибуте \"id\"\n",
    "id = geo_data_1.loc[geo_data_1['id'].duplicated(), 'id'].values\n",
    "\n",
    "for i in id:\n",
    "    print(geo_data_1.loc[geo_data_1['id'] == i])\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
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   "source": [
    "# Гистограммы числовых данных \n",
    "# второго региона\n",
    "geo_data_1.hist();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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    "# Анализ выбросов данных \n",
    "# второго региона\n",
    "for i in geo_data_1:\n",
    "    if i != 'id':\n",
    "        geo_data_0.boxplot(column=i);\n",
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    "# Анализ корреляции аттрибутов \n",
    "# датафрейма второго региона\n",
    "geo_data_1.corr()\n"
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   "source": [
    "# Коэффициент инфляции дисперсии аттрибутов второго региона\n",
    "# (5 и более - признак коррелирует со всеми остальными признаками \n",
    "# и его можно удалить и выразить через другие признаки)\n",
    "geo_data = geo_data_1.select_dtypes('number')#.columns\n",
    "vif = [variance_inflation_factor(geo_data, i) for i in range(len(geo_data.columns))]\n",
    "_ = sns.barplot(x=geo_data.columns.tolist(), y=vif)\n",
    "\n",
    "vif\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализ данных датафрейма третьего региона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 5 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   id       100000 non-null  object \n",
      " 1   f0       100000 non-null  float64\n",
      " 2   f1       100000 non-null  float64\n",
      " 3   f2       100000 non-null  float64\n",
      " 4   product  100000 non-null  float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 3.8+ MB\n",
      "None\n"
     ]
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       "      <th>product</th>\n",
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       "      <th>0</th>\n",
       "      <td>fwXo0</td>\n",
       "      <td>-1.146987</td>\n",
       "      <td>0.963328</td>\n",
       "      <td>-0.828965</td>\n",
       "      <td>27.758673</td>\n",
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       "      <th>1</th>\n",
       "      <td>WJtFt</td>\n",
       "      <td>0.262778</td>\n",
       "      <td>0.269839</td>\n",
       "      <td>-2.530187</td>\n",
       "      <td>56.069697</td>\n",
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       "      <th>2</th>\n",
       "      <td>ovLUW</td>\n",
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       "      <td>-5.586433</td>\n",
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       "      <td>-0.553760</td>\n",
       "      <td>0.930038</td>\n",
       "      <td>114.572842</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WPMUX</td>\n",
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       "      id        f0        f1        f2     product\n",
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     "execution_count": 20,
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   ],
   "source": [
    "# Проверка датафрейма третьего региона\n",
    "print(geo_data_2.info())\n",
    "geo_data_2.head() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.002023</td>\n",
       "      <td>-0.002081</td>\n",
       "      <td>2.495128</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.732045</td>\n",
       "      <td>1.730417</td>\n",
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       "      <td>44.749921</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.760004</td>\n",
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       "      <td>-11.970335</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>-1.162288</td>\n",
       "      <td>-1.174820</td>\n",
       "      <td>0.130359</td>\n",
       "      <td>59.450441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.009424</td>\n",
       "      <td>-0.009482</td>\n",
       "      <td>2.484236</td>\n",
       "      <td>94.925613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.158535</td>\n",
       "      <td>1.163678</td>\n",
       "      <td>4.858794</td>\n",
       "      <td>130.595027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.238262</td>\n",
       "      <td>7.844801</td>\n",
       "      <td>16.739402</td>\n",
       "      <td>190.029838</td>\n",
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      "text/plain": [
       "                  f0             f1             f2        product\n",
       "count  100000.000000  100000.000000  100000.000000  100000.000000\n",
       "mean        0.002023      -0.002081       2.495128      95.000000\n",
       "std         1.732045       1.730417       3.473445      44.749921\n",
       "min        -8.760004      -7.084020     -11.970335       0.000000\n",
       "25%        -1.162288      -1.174820       0.130359      59.450441\n",
       "50%         0.009424      -0.009482       2.484236      94.925613\n",
       "75%         1.158535       1.163678       4.858794     130.595027\n",
       "max         7.238262       7.844801      16.739402     190.029838"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Распределение значений датафрейма третьего региона\n",
    "geo_data_2.describe() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество уникальных идентификаторов: 99996 < Количество объектов 100000\n"
     ]
    }
   ],
   "source": [
    "# Соответствие количества уникальных идентификаторов \n",
    "# и количества объектов в датафрейме третьего региона\n",
    "# (сколько месторождений в регионе)\n",
    "geo_data_id = len(geo_data_2.id.unique())\n",
    "if geo_data_id == len(geo_data_2):\n",
    "    print(f'Количество уникальных идентификаторов: {geo_data_id} == Количество объектов {len(geo_data_2)}')\n",
    "else:\n",
    "    print(f'Количество уникальных идентификаторов: {geo_data_id} < Количество объектов {len(geo_data_2)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          id        f0        f1        f2     product\n",
      "28039  xCHr8  1.633027  0.368135 -2.378367    6.120525\n",
      "43233  xCHr8 -0.847066  2.101796  5.597130  184.388641\n",
      "\n",
      "          id        f0        f1        f2     product\n",
      "11449  VF7Jo  2.122656 -0.858275  5.746001  181.716817\n",
      "49564  VF7Jo -0.883115  0.560537  0.723601  136.233420\n",
      "\n",
      "          id        f0        f1        f2     product\n",
      "45404  KUPhW  0.231846 -1.698941  4.990775   11.716299\n",
      "55967  KUPhW  1.211150  3.176408  5.543540  132.831802\n",
      "\n",
      "          id        f0        f1        f2     product\n",
      "44378  Vcm5J -1.229484 -2.439204  1.222909  137.968290\n",
      "95090  Vcm5J  2.587702  1.986875  2.482245   92.327572\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Анализ объектов с дубликатами в аттрибуте \"id\"\n",
    "id = geo_data_2.loc[geo_data_2['id'].duplicated(), 'id'].values\n",
    "\n",
    "for i in id:\n",
    "    print(geo_data_2.loc[geo_data_2['id'] == i])\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
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   "source": [
    "# Гистограммы числовых данных \n",
    "# третьего региона\n",
    "geo_data_2.hist();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
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    "# Анализ выбросов данных \n",
    "# третьего региона\n",
    "for i in geo_data_2:\n",
    "    if i != 'id':\n",
    "        geo_data_0.boxplot(column=i);\n",
    "        plt.show() \n"
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    "# Анализ корреляции аттрибутов\n",
    "# датафрейма третьего региона\n",
    "geo_data_2.corr()\n"
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   "source": [
    "# Коэффициент инфляции дисперсии аттрибутов третьего региона\n",
    "# (5 и более - признак коррелирует со всеми остальными признаками \n",
    "# и его можно удалить и выразить через другие признаки)\n",
    "geo_data = geo_data_2.select_dtypes('number')#.columns\n",
    "vif = [variance_inflation_factor(geo_data, i) for i in range(len(geo_data.columns))]\n",
    "_ = sns.barplot(x=geo_data.columns.tolist(), y=vif)\n",
    "\n",
    "vif\n"
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   "metadata": {},
   "source": [
    "### Выводы из анализа данных датафреймов всех регионов"
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    "Предварительный анализ данных выявил следующее:\n",
    "1. Все три датафрейма содержат один текстовый оттрибут с идентификаторами скважин, а также три количественных числовых нецелевых аттрибута и один количественный числовой целевой аттрибут. \n",
    "1. В данных отсутствуют пропуски.\n",
    "1. Выявлены неявные дубликаты. Во всех датафреймах обнаружены от 6 до 10 объектов (скважин) с неуникальными идентификаторами в аттрибуте `id`. Все значения аттрибутов этих дубликатов значительно отличаются друг от друга. Учитывая отсутствие возможности выяснить причину наличия схожих идентификаторов и разницы значений их аттрибутов, предлагается оставить данные объекты.\n",
    "1. Аттрибут `id` с идентификаторами скважин имеет тип `Object` и около ста тысяч уникальных значений, за исключением 6-10 объектов, почти равных по количеству количеству всех объектов датафреймов. Для использования данного аттрибута в моделях, требуется его декодирование в числовые значения. Для линейной модели регрессии не подходит декодирование с помощью `LabelEncoder`, а с помощью OHE его декодировать накладно. Поэтому в модели линейной регрессии его использовать не желательно.\n",
    "1. Аттрибуты `f0` и `f1` датафрейма с объектами первого региона, а также `f0`, `f2` и `product` датафрейма с объектами второго региона не имеют нормального распределения. При этом, данные аттрибуты имеют значительные всплески вдали от основного центра распределения. Это нужно учесть при работе с моделями.\n",
    "1. Аттрибут `f2` датафреймов всех регионов имеет множество выбросов. Т.к. смысл данного атрибута не известен, предлагается не удалять объекты с выбросами данных в нем.\n",
    "1. Числовые нецелевые аттрибуты имеют близкие, но не достаточно схожие диапазоны значений. Требуется их стандартизировать.\n",
    "1. В датасетах разных регионов разные аттрибуты по разному коррелируют между собой. Так, в первом регионе обладают ниже средней прямой корреляцией признаки `f0` с `f1` и `f2` с `product`, а в третьем регионе только `f2` с `product`. Близкой к максимальной прямой корреляции в размере 0.999397 обладают аттрибуты `f2` с `product` во втором регионе. Коэффициент инфляции дисперсии этих двух аттрибутов близкий к 5, но не превышает это значение. Не смотря на практику удаления признаков из датафреймов в подобных случаях, аттрибут `f2` не следует удалять из датафрейма, т.к. сильная корреляция с целевым признаком позволяет более точно предсказывать этот самый целевой признак. \n",
    "\n",
    "Данные готовы для использования в машинном обучении перед которым требуется стандартизировать нецелевые числовые аттрибуты. "
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   "source": [
    "## Обучение и проверка модели"
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   "metadata": {},
   "source": [
    "Для достижения целей проекта требуется использовать регрессионную модель с кроссвалидацией, имеющей наилучшие показатели качества. В качестве средств автоматизации будет использованы технологии `Pipline` и `GridSearchSV`. Перед подбором параметров датафреймы будут разделены на целевые и нецелевые, а также на обучающие и треннировочные выборки в соотношении 75% и 25%. Обучающие выборки в рамках `GridSearchSV` будут разделены для кроссвалидации на обучающие и валидационные выборки в соотношении 75% и 25%. Аттрибут с идентификаторами будет исключен из всех выборок, а нецелевые числовые аттрибуты будут стандартизированы."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Полезные функции подготовки данных и подбора моделей и их параметров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Значение \"random_state\"\n",
    "STATE = 42\n",
    "\n",
    "# Массив лучших параметров моделей \n",
    "# всех регионов\n",
    "total_best_params = [] \n",
    "\n",
    "# Массив лучших результатов\n",
    "# всех регионов\n",
    "total_results= [] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функция подготовки данных перед подбором моделей и их параметров\n",
    "def data_preprocessing(geo_data):\n",
    "    \n",
    "    # Разделение датафреймов на целевую и нецелевую выборку\n",
    "    features = geo_data.drop(['id', 'product'], axis=1)\n",
    "    target = geo_data['product']\n",
    "\n",
    "    # Значение \"random_state\"\n",
    "    state = 42\n",
    "\n",
    "    # Разделение целевой и нецелевой выборки \n",
    "    # на обучающие и тестовые выборки\n",
    "    features_train,  features_test, target_train, target_test = train_test_split(\n",
    "        features, \n",
    "        target, \n",
    "        test_size=.25, \n",
    "        random_state=state\n",
    "    )\n",
    "    \n",
    "    return features_train,  features_test, target_train, target_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функция создания структуры пайплайна\n",
    "def params_and_model_selection(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    model_params\n",
    "):\n",
    "\n",
    "    # Значение \"random_state\"\n",
    "    state = 42\n",
    "    \n",
    "    # Удаление аттрибута \"id\"\n",
    "    #features_train = features_train.drop('id', axis=1)\n",
    "    \n",
    "    # Стандартизация числовых значений\n",
    "    numeric_transformer = make_pipeline(\n",
    "        StandardScaler()\n",
    "    )\n",
    "    \n",
    "    # Шаг препроцессинга в Пайплайне\n",
    "    preprocessor = make_column_transformer(\n",
    "        (numeric_transformer, features_train.columns)\n",
    "    )\n",
    "    \n",
    "    # Pipeline\n",
    "    pipe = Pipeline([\n",
    "        ('preprocessor', preprocessor), \n",
    "        ('regressor', model_params[0]['regressor'][0])\n",
    "    ])\n",
    "    pipe.fit(features_train, target_train)\n",
    "    \n",
    "    # HalvingGridSearchCV\n",
    "    # (о подборе оптимальных параметров:\n",
    "    # https://scikit-learn.ru/3-2-tuning-the-hyper-parameters-of-an-estimator/)\n",
    "    grid = HalvingGridSearchCV(\n",
    "        pipe, \n",
    "        model_params, \n",
    "        cv=4, # параметр KFold для кроссвалидации (обучющая и валидационная выборки 75:25)\n",
    "        n_jobs=-1, # количество параллельно выполняемых заданий (-1 - задействованы все процессоры)\n",
    "        # если scoring=mean_squared_error, то в результате \"nan\"\n",
    "        scoring='neg_root_mean_squared_error', # корень из средней квадратичной ошибки (чувствительна к выбросам, в результате реальные, а не квадратные значения)\n",
    "        random_state=state\n",
    "    )\n",
    "    grid.fit(features_train, target_train)\n",
    "    \n",
    "    return grid\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модель машинного обучения для первого региона"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Подготовка выборок из датафрейма с данными первого региона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>-0.557623</td>\n",
       "      <td>2.121187</td>\n",
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       "      <th>80184</th>\n",
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       "      <td>-0.463156</td>\n",
       "      <td>-1.347584</td>\n",
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       "    <tr>\n",
       "      <th>19864</th>\n",
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       "      <td>-0.534917</td>\n",
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       "             f0        f1        f2\n",
       "75721  0.599283 -0.557623  2.121187\n",
       "80184  0.739017 -0.463156 -1.347584\n",
       "19864  1.422743 -0.534917  3.718798\n",
       "76699  1.580244 -0.238458  2.805149\n",
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Подготовка выборок из датафрейма \n",
    "# с данными первого региона\n",
    "features_0_train,  features_0_test, target_0_train, target_0_test = data_preprocessing(geo_data_0)\n",
    "\n",
    "features_0_test.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Модель LinearRegression с использованием технологий Pipline и GridSearchSV для первого региона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Модель   : LinearRegression\n",
      "RMSE     : -37.67176560084854\n",
      "Параметры:\n",
      " Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(transformers=[('pipeline',\n",
      "                                                  Pipeline(steps=[('standardscaler',\n",
      "                                                                   StandardScaler())]),\n",
      "                                                  Index(['f0', 'f1', 'f2'], dtype='object'))])),\n",
      "                ('regressor', LinearRegression())])\n"
     ]
    }
   ],
   "source": [
    "# LinearRegression\n",
    "grids_0 = params_and_model_selection(\n",
    "    features_0_train, \n",
    "    features_0_test, \n",
    "    target_0_train, \n",
    "    target_0_test, \n",
    "    [{\n",
    "        'regressor': [LinearRegression()], # score: R^2\n",
    "        'regressor__fit_intercept': [True, False]\n",
    "    }]\n",
    ")\n",
    "\n",
    "print('Модель   :', 'LinearRegression')\n",
    "print('RMSE     :', grids_0.best_score_)\n",
    "print('Параметры:\\n', grids_0.best_estimator_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Предсказания для первого региона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Регион                           : 1\n",
      "Средний предсказанный запас сырья: 92.39879990657768\n",
      "RMSE                             : 37.75660035026169\n"
     ]
    }
   ],
   "source": [
    "# Предсказания для первого региона\n",
    "#predict_0 = grids_0.predict(features_0_test.drop('id', axis=1))\n",
    "predict_0 = grids_0.predict(features_0_test)\n",
    "total_results.append([\n",
    "    1, \n",
    "    predict_0.mean(), \n",
    "    mean_squared_error(target_0_test, predict_0, squared=False)\n",
    "])\n",
    "print('Регион                           :', total_results[0][0])\n",
    "print('Средний предсказанный запас сырья:', total_results[0][1])\n",
    "print('RMSE                             :', total_results[0][2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модель машинного обучения для второго региона"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Подготовка выборок из датафрейма с данными второго региона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>98980</th>\n",
       "      <td>9.296378</td>\n",
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       "      <td>1.999544</td>\n",
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       "    <tr>\n",
       "      <th>69824</th>\n",
       "      <td>15.836796</td>\n",
       "      <td>1.952969</td>\n",
       "      <td>4.989288</td>\n",
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       "      <th>9928</th>\n",
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       "      <th>95621</th>\n",
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       "    </tr>\n",
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       "              f0        f1        f2\n",
       "98980   9.296378 -4.480220  1.999544\n",
       "69824  15.836796  1.952969  4.989288\n",
       "9928   -1.594937 -0.407367  3.003358\n",
       "75599  12.335752 -6.508978  2.001396\n",
       "95621  -4.954638 -8.026328  1.001472"
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     "execution_count": 34,
     "metadata": {},
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   ],
   "source": [
    "# Подготовка выборок из датафрейма \n",
    "# с данными второго региона\n",
    "features_1_train,  features_1_test, target_1_train, target_1_test = data_preprocessing(geo_data_1)\n",
    "#features_1_train = features_1_train.drop('f2', axis=1)\n",
    "#features_1_test = features_1_test.drop('f2', axis=1)\n",
    "\n",
    "features_1_train.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Модель LinearRegression с использованием технологий Pipline и GridSearchSV для второго региона с аттрибутом `f2`, сильно коррелирующим с целевым признаком"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Модель   : LinearRegression\n",
      "RMSE     : -0.8904633868175174\n",
      "Параметры:\n",
      " Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(transformers=[('pipeline',\n",
      "                                                  Pipeline(steps=[('standardscaler',\n",
      "                                                                   StandardScaler())]),\n",
      "                                                  Index(['f0', 'f1', 'f2'], dtype='object'))])),\n",
      "                ('regressor', LinearRegression())])\n"
     ]
    }
   ],
   "source": [
    "# LinearRegression\n",
    "grids_1 = params_and_model_selection(\n",
    "    features_1_train, \n",
    "    features_1_test, \n",
    "    target_1_train, \n",
    "    target_1_test, \n",
    "    [{\n",
    "        'regressor': [LinearRegression()], # score: R^2\n",
    "        'regressor__fit_intercept': [True, False]\n",
    "    }]\n",
    ")\n",
    "\n",
    "print('Модель   :', 'LinearRegression')\n",
    "print('RMSE     :', grids_1.best_score_)\n",
    "print('Параметры:\\n', grids_1.best_estimator_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Предсказания для второго региона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Регион                           : 1\n",
      "Средний предсказанный запас сырья: 92.39879990657768\n",
      "RMSE                             : 37.75660035026169\n"
     ]
    }
   ],
   "source": [
    "# Предсказания для второго региона\n",
    "#predict_1 = grids_1.predict(features_1_test.drop('id', axis=1))\n",
    "predict_1 = grids_1.predict(features_1_test)\n",
    "total_results.append([\n",
    "    2, \n",
    "    predict_1.mean(), \n",
    "    mean_squared_error(target_1_test, predict_1, squared=False)\n",
    "])\n",
    "print('Регион                           :', total_results[0][0])\n",
    "print('Средний предсказанный запас сырья:', total_results[0][1])\n",
    "print('RMSE                             :', total_results[0][2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модель машинного обучения для третьего региона"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Подготовка выборок из датафрейма с данными третьего региона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "             f0        f1        f2\n",
       "75721  2.111118 -1.679773  3.112240\n",
       "80184  0.734759  0.747788  3.670879\n",
       "19864 -2.513109  0.844631 -4.922889\n",
       "76699 -2.035301 -1.522988  5.072839\n",
       "92991  2.744145  1.429952 -1.372661"
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     "execution_count": 37,
     "metadata": {},
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   ],
   "source": [
    "# Подготовка выборок из датафрейма \n",
    "# с данными третьего региона\n",
    "features_2_train,  features_2_test, target_2_train, target_2_test = data_preprocessing(geo_data_2)\n",
    "\n",
    "features_2_test.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Модель LinearRegression с использованием технологий Pipline и GridSearchSV для третьего региона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Модель   : LinearRegression\n",
      "RMSE     : -40.028747787073236\n",
      "Параметры:\n",
      " Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(transformers=[('pipeline',\n",
      "                                                  Pipeline(steps=[('standardscaler',\n",
      "                                                                   StandardScaler())]),\n",
      "                                                  Index(['f0', 'f1', 'f2'], dtype='object'))])),\n",
      "                ('regressor', LinearRegression())])\n"
     ]
    }
   ],
   "source": [
    "# Модель LinearRegression с использованием \n",
    "# технологий Pipline и GridSearchSV для третьего региона\n",
    "grids_2 = params_and_model_selection(\n",
    "    features_2_train, \n",
    "    features_2_test, \n",
    "    target_2_train, \n",
    "    target_2_test, \n",
    "    [{\n",
    "        'regressor': [LinearRegression()], # score: R^2\n",
    "        'regressor__fit_intercept': [True, False]\n",
    "    }]\n",
    ")\n",
    "\n",
    "print('Модель   :', 'LinearRegression')\n",
    "print('RMSE     :', grids_2.best_score_)\n",
    "print('Параметры:\\n', grids_2.best_estimator_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Предсказания для третьего региона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Регион                           : 1\n",
      "Средний предсказанный запас сырья: 92.39879990657768\n",
      "RMSE                             : 37.75660035026169\n"
     ]
    }
   ],
   "source": [
    "# Предсказания для третьего региона\n",
    "#predict_2 = grids_2.predict(features_2_test.drop('id', axis=1))\n",
    "predict_2 = grids_2.predict(features_2_test)\n",
    "total_results.append([\n",
    "    3, \n",
    "    predict_2.mean(), \n",
    "    mean_squared_error(target_2_test, predict_2, squared=False)\n",
    "])\n",
    "print('Регион                           :', total_results[0][0])\n",
    "print('Средний предсказанный запас сырья:', total_results[0][1])\n",
    "print('RMSE                             :', total_results[0][2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализ результатов обучения и проверки моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Регион</th>\n",
       "      <th>Среднее значение</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>-3 sigma</th>\n",
       "      <th>-1 sigma</th>\n",
       "      <th>+1 sigma</th>\n",
       "      <th>+3 sigma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>92.398800</td>\n",
       "      <td>37.756600</td>\n",
       "      <td>-20.871001</td>\n",
       "      <td>54.642200</td>\n",
       "      <td>130.155400</td>\n",
       "      <td>205.668601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>68.712878</td>\n",
       "      <td>0.890280</td>\n",
       "      <td>66.042038</td>\n",
       "      <td>67.822598</td>\n",
       "      <td>69.603158</td>\n",
       "      <td>71.383718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>94.771024</td>\n",
       "      <td>40.145872</td>\n",
       "      <td>-25.666593</td>\n",
       "      <td>54.625152</td>\n",
       "      <td>134.916896</td>\n",
       "      <td>215.208641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Регион  Среднее значение       RMSE   -3 sigma   -1 sigma    +1 sigma  \\\n",
       "0       1         92.398800  37.756600 -20.871001  54.642200  130.155400   \n",
       "1       2         68.712878   0.890280  66.042038  67.822598   69.603158   \n",
       "2       3         94.771024  40.145872 -25.666593  54.625152  134.916896   \n",
       "\n",
       "     +3 sigma  \n",
       "0  205.668601  \n",
       "1   71.383718  \n",
       "2  215.208641  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сводная таблица результатов моделей всех регионов\n",
    "total_results = pd.DataFrame(data=total_results, columns=['region', 'average', 'rmse'])\n",
    "total_results['minus_3_sigma'] = total_results['average'] - total_results['rmse']*3\n",
    "total_results['minus_1_sigma'] = total_results['average'] - total_results['rmse']\n",
    "total_results['plus_1_sigma'] = total_results['average'] + total_results['rmse']\n",
    "total_results['plus_3_sigma'] = total_results['average'] + total_results['rmse']*3\n",
    "\n",
    "total_results.rename(columns = {\n",
    "    'region':'Регион', \n",
    "    'average':'Среднее значение', \n",
    "    'rmse':'RMSE', \n",
    "    'minus_3_sigma':'-3 sigma', \n",
    "    'minus_1_sigma':'-1 sigma', \n",
    "    'plus_1_sigma':'+1 sigma', \n",
    "    'plus_3_sigma':'+3 sigma'\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводы:\n",
    "1. Третий регион имеет наиболее высокое предсказанное среднее значение целевого аттрибута `product` (предполагаемый объем запасов нефти), равное 95. В этом же регионе самый большое значение корня из средней квадратичной ошибки (`RMSE`, далее $\\sigma$) предсказаний аттрибута `product`, равное 40.\n",
    "1. Во втором регионе самое низкое значение целевого аттрибута `product`, равное 69. Это почти на четверть меньше, чем в других регионах. При этом, во втором регионе самое низкое значение разброса $\\sigma$ предсказаний аттрибута `product`. Оно равно 0.89, что почти в 40 раз меньше, чем в других регионах. Благодаря минимальному отклонению предсказанных значений от реальных, второй регион наиболее предсказуемый. В нем самые большие значения на нижней границе одной $\\sigma$ и нет отрицательных значений на нижней границе трех $\\sigma$.\n",
    "1. В первом и втором регионах нижняя граница трех $\\sigma$ находится существенно ниже 0. Возможно, это следует трактовать как наличие некоторого количества скважин без нефти.\n",
    "\n",
    "Предварительно можно утверждать, что, по результатам предсказаний моделей линейной регрессии, второй регион является самым предсказуемым, но низкодоходным, а третий самым доходным и рискованным.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка к расчёту прибыли"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Константы проекта"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Константы проекта\n",
    "OBJECTS_UNDER_STUDY_NUM = 500 # количество исследуемых объектов, участвующих в обучении модели\n",
    "BEST_OBJECTS_NUM = 200 # количество лучших объектов в регионе, которые будут разработаны (из 500 оцененных)\n",
    "TOTAL_BUDGET = 10000000000 # бюджет проекта для одного региона\n",
    "REVENUE_PER_UNIT = 450000 # доходность одной единицы продукта (= 450 за баррель * 1000 баррелей в одной единице объекта \"total\")\n",
    "MAX_PROBABILITY_OF_LOSS = .025 # максимально допустимая веросятность убытков\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Хорошо, что в названиях константных переменных использовались только большие буквы!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Точка безубыточности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точка безубыточности проекта равна добыче 111.11111111111111 баррелей в одной из 200 скважин одного региона.\n"
     ]
    }
   ],
   "source": [
    "# Точка безубыточности равна частному \n",
    "# от деления всего бюджета проекта для одного региона \n",
    "# и стоисомти 1000 баррелей нефти, \n",
    "# деленные на количество разрабатываемых скважин \n",
    "# в рамках одного региона\n",
    "break_event_point = (TOTAL_BUDGET / REVENUE_PER_UNIT) / BEST_OBJECTS_NUM\n",
    "print(f'Точка безубыточности проекта равна добыче {break_event_point} баррелей в одной из 200 скважин одного региона.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -18.712311\n",
       "1   -42.398233\n",
       "2   -16.340087\n",
       "Name: average, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Разница между объемами добычи, \n",
    "# требуемыми для безубыточности проекта \n",
    "# и средними значениями предсказанных \n",
    "# объемов добычи нефти во всех скважинах \n",
    "# разных регионов\n",
    "total_results['average'] - break_event_point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Во всех регионах средние предсказанные значения объемов добычи нефти в одной сважине меньше минимум на 16 тыс. баррелей, чем требуется для безубыточности проекта."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Разница между объемами добычи, требуемыми для безубыточности проекта и средними значениями объемов добычи нефти в 200 скважинах с предсказанным максимальным объемом в разных регионов.\n",
      "\n",
      "Первый регион: 43.29620532962386\n",
      "Второй регион: 27.632645722212004\n",
      "Третий регион: 37.53312793016613\n"
     ]
    }
   ],
   "source": [
    "# Разница между объемами добычи, \n",
    "# требуемыми для безубыточности проекта \n",
    "# и средними значениями объемов добычи нефти \n",
    "# в 200 скважинах с предсказанным \n",
    "# максимальным объемом в разных регионов\n",
    "print('Разница между объемами добычи, \\\n",
    "требуемыми для безубыточности проекта и средними значениями \\\n",
    "объемов добычи нефти в 200 скважинах с предсказанным \\\n",
    "максимальным объемом в разных регионов.\\n')\n",
    "print('Первый регион:', pd.Series(predict_0).sort_values(ascending=False).head(200).mean() - break_event_point)\n",
    "print('Второй регион:', pd.Series(predict_1).sort_values(ascending=False).head(200).mean() - break_event_point)\n",
    "print('Третий регион:', pd.Series(predict_2).sort_values(ascending=False).head(200).mean() - break_event_point)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Средние значения 200 скважин с максимальным предсказанным объемом нефти во всех регионах превышают точку безубыточности. Лидирует первый регион с превышением в 43 тыс. баррелей."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Итоги подготовки к расчету прибыли"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для безубыточности проекта минимальный объем добычи в одной скважине должен быть не менее 111 тыс. баррелей. Средние же значения предсказанных объемов добычи в одной скважине всех регионов меньше этого значения минимум на 16 тыс. баррелей. Однако, средние значения 200 скважин с максимальным предсказанным объемом нефти во всех регионах превышают точку безубыточности. Лидирует первый регион."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Функция расчета прибыли"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функция расчета прибыли\n",
    "def profit_calc(df):\n",
    "    # df = columns 'id', 'product', 'predict'\n",
    "    \n",
    "    # Выбор 200 скважин в первом регионе\n",
    "    # с максимальным предсказанным значением \n",
    "    # запасов нефти\n",
    "    df_200 = df.sort_values(\n",
    "        by='predict', \n",
    "        ascending=False\n",
    "    ).head(BEST_OBJECTS_NUM)\n",
    "    \n",
    "    # Расчет прибыли целевых значений, \n",
    "    # соответствующих 200 предсказаниям \n",
    "    # с максимальными значениями запасов нефти\n",
    "    return (df_200['product'].sum() * REVENUE_PER_UNIT) - TOTAL_BUDGET\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Расчёт прибыли и рисков "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Функция расчета доходов и убытков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функция расчета доходов и убытков\n",
    "def profit_and_loss(target_test, predict):\n",
    "    \n",
    "    # Создание единого датафрейма\n",
    "    df = pd.DataFrame({\n",
    "        'product':target_test.values, \n",
    "        'predict':predict\n",
    "    })\n",
    "    \n",
    "    # Значение для random_state\n",
    "    state = np.random.RandomState(42)\n",
    "    # Массив доходности после применения техники bootstrep\n",
    "    profit_bootstrep = []\n",
    "    \n",
    "    for i in range(0, 1000, 1):\n",
    "        df_sample = df.sample(\n",
    "            n=500, \n",
    "            replace=True, \n",
    "            random_state=state\n",
    "        )\n",
    "        \n",
    "        profit_bootstrep.append(profit_calc(df_sample))\n",
    "    \n",
    "    profit_bootstrep = pd.Series(profit_bootstrep)\n",
    "    # Средний доход\n",
    "    mean_profit = int(profit_bootstrep.mean())\n",
    "    # Нижний квантиль 2.5% от 95% доверительного интервала\n",
    "    lower_profit = int(profit_bootstrep.quantile(.025))\n",
    "    # Верхний квантиль 97.5% от 95% доверительного интервала\n",
    "    upper_profit = int(profit_bootstrep.quantile(.975))\n",
    "    # Средний убыток\n",
    "    #mean_loss = int(profit_bootstrep[profit_bootstrep < 0].mean())\n",
    "    # Вероятность убытка\n",
    "    #probability_of_loss = len(profit_bootstrep[profit_bootstrep < 0]) / len(profit_bootstrep)\n",
    "    probability_of_loss = (profit_bootstrep.values < 0).mean()\n",
    "    \n",
    "    print(f'95% доверительный интервал доходности находится в пределах от {lower_profit} до {upper_profit}')\n",
    "    print('Средний доход     :', mean_profit)\n",
    "    #print('Средний убыток    :', mean_loss)\n",
    "    print(f'Вероятность убытка: {probability_of_loss*100}%')\n",
    "    \n",
    "    return mean_profit, probability_of_loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Массив средней доходности и вероянтости убытков\n",
    "# mppl - mean profit and probability of loss\n",
    "mean_profits = []\n",
    "probabilities_of_losses = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Расчет ожидаемой средней прибыли и вероятности убытков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% доверительный интервал доходности находится в пределах от -110467895 до 897460327\n",
      "Средний доход     : 399575478\n",
      "Вероятность убытка: 6.0%\n"
     ]
    }
   ],
   "source": [
    "# Первый регион\n",
    "mean_profit, probabilitу_of_loss = profit_and_loss(target_0_test, predict_0)\n",
    "mean_profits.append(mean_profit)\n",
    "probabilities_of_losses.append(probabilitу_of_loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% доверительный интервал доходности находится в пределах от 61684479 до 845340177\n",
      "Средний доход     : 452048890\n",
      "Вероятность убытка: 1.5%\n"
     ]
    }
   ],
   "source": [
    "# Второй регион\n",
    "mean_profit, probabilitу_of_loss = profit_and_loss(target_1_test, predict_1)\n",
    "mean_profits.append(mean_profit)\n",
    "probabilities_of_losses.append(probabilitу_of_loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% доверительный интервал доходности находится в пределах от -144766727 до 888390403\n",
      "Средний доход     : 375009902\n",
      "Вероятность убытка: 8.0%\n"
     ]
    }
   ],
   "source": [
    "# Третий регион\n",
    "mean_profit, probabilitу_of_loss = profit_and_loss(target_2_test, predict_2)\n",
    "mean_profits.append(mean_profit)\n",
    "probabilities_of_losses.append(probabilitу_of_loss)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выводы расчета прибыли и рисков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Для инвестирования предлагается выбрать первый регион. В этом регионе ожидаемая доходность 452048890, а верояность убытка 1.5%. Среди регионов с вероятностью убытка, находящегося в пределах допустимого значения 2.5%, в данном регионе самая высокая ожидаемая прибыль.\n"
     ]
    }
   ],
   "source": [
    "# Выводы расчета прибыли и рисков \n",
    "mppl_df = pd.DataFrame(\n",
    "    {\n",
    "        'profit':mean_profits, \n",
    "        'probability_of_loss':probabilities_of_losses\n",
    "    }\n",
    ")\n",
    "mppl_df = mppl_df.loc[mppl_df['probability_of_loss'] < MAX_PROBABILITY_OF_LOSS]\n",
    "mppl_df = mppl_df.loc[mppl_df['profit'] == mppl_df['profit'].max()]\n",
    "\n",
    "num = {1:'первый', 2:'второй', 3:'третий'}\n",
    "\n",
    "print(f\"Для инвестирования предлагается выбрать {num[mppl_df.index[0]]} регион. \\\n",
    "В этом регионе ожидаемая доходность {mppl_df.loc[mppl_df.index[0], 'profit']}, \\\n",
    "а верояность убытка {mppl_df.loc[mppl_df.index[0], 'probability_of_loss']*100}%. \\\n",
    "Среди регионов с вероятностью убытка, находящегося \\\n",
    "в пределах допустимого значения {MAX_PROBABILITY_OF_LOSS*100}%, \\\n",
    "в данном регионе самая высокая ожидаемая прибыль.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Выводы проекта"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Цели проекта достигнуты. Предложен регион для инвестирования. Выбор региона обоснован. Для достижения целей проекта были решены следующие задачи:\n",
    "1. Загружены и подготовлены данные. Поясните порядок действий.\n",
    "1. Обучены и проверены модели для каждого региона\n",
    "1. Проведена подготовка к расчёту прибыли:\n",
    "1. Рассчитаны риски и прибыль для каждого региона."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Чек-лист готовности проекта"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поставьте 'x' в выполненных пунктах. Далее нажмите Shift+Enter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  Jupyter Notebook открыт\n",
    "- [x]  Весь код выполняется без ошибок\n",
    "- [x]  Ячейки с кодом расположены в порядке исполнения\n",
    "- [x]  Выполнен шаг 1: данные подготовлены\n",
    "- [x]  Выполнен шаг 2: модели обучены и проверены\n",
    "    - [x]  Данные корректно разбиты на обучающую и валидационную выборки\n",
    "    - [x]  Модели обучены, предсказания сделаны\n",
    "    - [x]  Предсказания и правильные ответы на валидационной выборке сохранены\n",
    "    - [x]  На экране напечатаны результаты\n",
    "    - [x]  Сделаны выводы\n",
    "- [x]  Выполнен шаг 3: проведена подготовка к расчёту прибыли\n",
    "    - [x]  Для всех ключевых значений созданы константы Python\n",
    "    - [x]  Посчитано минимальное среднее количество продукта в месторождениях региона, достаточное для разработки\n",
    "    - [x]  По предыдущему пункту сделаны выводы\n",
    "    - [x]  Написана функция расчёта прибыли\n",
    "- [x]  Выполнен шаг 4: посчитаны риски и прибыль\n",
    "    - [x]  Проведена процедура *Bootstrap*\n",
    "    - [x]  Все параметры бутстрепа соответствуют условию\n",
    "    - [x]  Найдены все нужные величины\n",
    "    - [x]  Предложен регион для разработки месторождения\n",
    "    - [x]  Выбор региона обоснован"
   ]
  }
 ],
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